Investigation of featurization approaches for supervised machine learning in X-ray spectroscopy

Machine learning (ML) has been revolutionizing the analysis of materials characterization data. In X-ray absorption spectroscopy (XAS) and X-ray emission spectroscopy (XES), for instance, ML is used to extract critical structural information and accelerate the interpretation of spectra. While most ML models have utilized the raw spectra as the input, less emphasis has been placed on the preprocessing of spectra to derive alternative inputs that can potentially enhance model performance. In this talk, we will benchmark reduced dimension features, e.g., peaks and principal components, and overcomplete representations to identify the optimal representation of spectroscopy data for supervised machine learning approaches. The system of interest is a cathode material for Li-ion battery, LiNi_xMn_yCo_zO₂ (NMC), which is well-known for its high energy densit and long-term cyclability. The performance of these input transformations will be assessed for both XAS, which contains key information about oxidation states and local environment, as well as XES, which provides additional information about electronic states. We will demonstrate that such featurization can significantly enhance not only the prediction accuracy, but also the interpretability of ML models.